Conventionally, data aggregation is performed by dividing an area into meshes. FIGS. 31 and 32 are diagrams illustrating examples of such data aggregation. In FIG. 31, a certain region 10a is divided in meshes per town-and-district (town-and-Chome) and FIG. 32 is divided in meshes each merged of four adjacent ones of the meshes in FIG. 31. <0} In FIGS. 31 and 32, the darker the color of a mesh is, the larger the population in that mesh is.
Comparing FIGS. 31 and 32, for the same region 10a, different results are obtained depending on how the division into meshes is made. For example, in FIG. 31, the north of a railroad 10b appears to be more populated. On the contrary, in FIG. 32, the south of the railroad 10b appears to be more populated. Such a change in the obtained results depending on how the division into meshes is made is called the modifiable areal unit problem.
For the above mentioned conventional technique, another conventional technique, in which an association rule is set and an area maximized with a confidence of the association rule is detected, exists. In this conventional technique, because the way of dividing into meshes is not predetermined, the modifiable areal unit problem does not occur.
For example, see: SUGIURA, Yoshio, “Geographical Spacial Analysis (Chiri-Kukan-Bunseki)”, Asakura Books, 2003; FUKUDA, T., et. al, “Data mining using two-dimensional optimized association rules”, SIGMOD '96; and RASTOGI, R., “Mining optimized association rules with categorical and numeric attribute”, IEEE Trans. On Knowledge and Data Engineering, Vol. 14, Issue 1, 2002.
However, the above described conventional technique has the problem of not being able to detect an area having a locally maximum confidence related to the association rule.
FIG. 33 is a diagram illustrating a concept of local maxima. For example, the horizontal axis corresponds to position and the vertical axis corresponds to confidence. Points 1a to 1d in FIG. 33 represent local maximum points. The local maximum point 1a is a point where the value is maximum in a neighborhood 2a. At the local maximum point 1a, the value changes from increase to decrease. Similarly, the local maximum points 1b to 1d are points where the value becomes maximum in neighborhoods 2b to 2d, respectively.
In the conventional technique, an area having the maximum confidence is detected. The problem in the conventional technique is that as illustrated in FIG. 33, an area corresponding to the local maximum point 1a is detected in the conventional technique because confidence becomes the largest at the local maximum point 1a. However, in the conventional technique, areas corresponding to the remaining local maximum points 1b to 1d are not detectable.
For example, in an example where data are aggregated for areas in which many people, who have taken taxis, take long distance rides, it will be convenient if not only an area in which a possibility of a long distance ride being taken is the largest, but also an area in which the possibility is locally maximum are concurrently presentable. This is because, for example, in the area with the locally maximum possibility, the possibility of being able to pick up a long distance ride customer is not the largest, but there is a higher possibility, than in a current spot where a taxi is currently traveling, of being able to pick up a long distance ride customer near the current spot. However, in the conventional technique, only an area in which the possibility of a long distance ride being taken is the largest is detectable.